{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # This mounts your Google Drive to the Colab VM.\n",
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')\n",
    "\n",
    "# # TODO: Enter the foldername in your Drive where you have saved the unzipped\n",
    "# # assignment folder, e.g. 'cs231n/assignments/assignment1/'\n",
    "# FOLDERNAME = None\n",
    "# assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n",
    "\n",
    "# # Now that we've mounted your Drive, this ensures that\n",
    "# # the Python interpreter of the Colab VM can load\n",
    "# # python files from within it.\n",
    "# import sys\n",
    "# sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))\n",
    "\n",
    "# # This downloads the CIFAR-10 dataset to your Drive\n",
    "# # if it doesn't already exist.\n",
    "# %cd /content/drive/My\\ Drive/$FOLDERNAME/cs231n/datasets/\n",
    "# !bash get_datasets.sh\n",
    "# %cd /content/drive/My\\ Drive/$FOLDERNAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization, proposed by [1] in 2015.\n",
    "\n",
    "To understand the goal of batch normalization, it is important to first recognize that machine learning methods tend to perform better with input data consisting of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features. This will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance, since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, they propose to insert into the network layers that normalize batches. At training time, such a layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n",
      "\tYou will need to compile a Cython extension for a portion of this assignment.\n",
      "\tThe instructions to do this will be given in a section of the notebook below.\n"
     ]
    }
   ],
   "source": [
    "# Setup cell.\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (10.0, 8.0)  # Set default size of plots.\n",
    "plt.rcParams[\"image.interpolation\"] = \"nearest\"\n",
    "plt.rcParams[\"image.cmap\"] = \"gray\"\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\"Returns relative error.\"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print(f\"  means: {x.mean(axis=axis)}\")\n",
    "    print(f\"  stds:  {x.std(axis=axis)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (49000, 3, 32, 32)\n",
      "y_train: (49000,)\n",
      "X_val: (1000, 3, 32, 32)\n",
      "y_val: (1000,)\n",
      "X_test: (1000, 3, 32, 32)\n",
      "y_test: (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR-10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "    print(f\"{k}: {v.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization: Forward Pass\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above in [1] may be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means: [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:  [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means: [2.22044605e-17 8.16013923e-17 4.46864767e-17]\n",
      "  stds:  [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means: [11. 12. 13.]\n",
      "  stds:  [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network.\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "\n",
    "# Means should be close to zero and stds close to one.\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma.\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means: [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:  [1.01531427 1.01238373 0.97819987]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization: Backward Pass\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.7029241291468676e-09\n",
      "dgamma error:  7.420414216247087e-13\n",
      "dbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass.\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "\n",
    "# You should expect to see relative errors between 1e-13 and 1e-8.\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization: Alternative Backward Pass\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n",
    "\n",
    "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "\n",
    "we first calculate the mean $\\mu$ and variance $v$.\n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n",
    "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n",
    "\n",
    "\\begin{align}\n",
    "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n",
    "& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/batchnorm_graph.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "The unknown/hard part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n",
    "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n",
    "$\\frac{\\partial \\sigma}{\\partial v}$, \n",
    "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n",
    "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n",
    "\n",
    "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  9.20004371222927e-13\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 0.95x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully Connected Networks with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "**Hint:** You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 3.07e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 4.44e-08\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 9.06e-11\n",
      "beta1 relative error: 7.85e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 7.47e-09\n",
      "gamma2 relative error: 3.35e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 5.55e-09\n",
      "b3 relative error: 1.73e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 4.67e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization for Deep Networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver with batch norm:\n",
      "(Iteration 1 / 882) loss: 2.308638\n",
      "(Epoch 0 / 9) train acc: 0.101000; val_acc: 0.101000\n",
      "(Iteration 21 / 882) loss: 1.993306\n",
      "(Iteration 41 / 882) loss: 1.760457\n",
      "(Iteration 61 / 882) loss: 1.622022\n",
      "(Iteration 81 / 882) loss: 1.518698\n",
      "(Epoch 1 / 9) train acc: 0.450000; val_acc: 0.436000\n",
      "(Iteration 101 / 882) loss: 1.468346\n",
      "(Iteration 121 / 882) loss: 1.486299\n",
      "(Iteration 141 / 882) loss: 1.410354\n",
      "(Iteration 161 / 882) loss: 1.403525\n",
      "(Iteration 181 / 882) loss: 1.323420\n",
      "(Epoch 2 / 9) train acc: 0.501000; val_acc: 0.492000\n",
      "(Iteration 201 / 882) loss: 1.386309\n",
      "(Iteration 221 / 882) loss: 1.271042\n",
      "(Iteration 241 / 882) loss: 1.399472\n",
      "(Iteration 261 / 882) loss: 1.284285\n",
      "(Iteration 281 / 882) loss: 1.329060\n",
      "(Epoch 3 / 9) train acc: 0.554000; val_acc: 0.516000\n",
      "(Iteration 301 / 882) loss: 1.262019\n",
      "(Iteration 321 / 882) loss: 1.276179\n",
      "(Iteration 341 / 882) loss: 1.187676\n",
      "(Iteration 361 / 882) loss: 1.185973\n",
      "(Iteration 381 / 882) loss: 1.220460\n",
      "(Epoch 4 / 9) train acc: 0.594000; val_acc: 0.512000\n",
      "(Iteration 401 / 882) loss: 1.256680\n",
      "(Iteration 421 / 882) loss: 1.168408\n",
      "(Iteration 441 / 882) loss: 1.140493\n",
      "(Iteration 461 / 882) loss: 1.228569\n",
      "(Iteration 481 / 882) loss: 1.196397\n",
      "(Epoch 5 / 9) train acc: 0.592000; val_acc: 0.540000\n",
      "(Iteration 501 / 882) loss: 1.120051\n",
      "(Iteration 521 / 882) loss: 1.144556\n",
      "(Iteration 541 / 882) loss: 1.153797\n",
      "(Iteration 561 / 882) loss: 1.119966\n",
      "(Iteration 581 / 882) loss: 1.110922\n",
      "(Epoch 6 / 9) train acc: 0.609000; val_acc: 0.552000\n",
      "(Iteration 601 / 882) loss: 1.058328\n",
      "(Iteration 621 / 882) loss: 1.121693\n",
      "(Iteration 641 / 882) loss: 1.002446\n",
      "(Iteration 661 / 882) loss: 1.055153\n",
      "(Iteration 681 / 882) loss: 1.104824\n",
      "(Epoch 7 / 9) train acc: 0.650000; val_acc: 0.537000\n",
      "(Iteration 701 / 882) loss: 1.075425\n",
      "(Iteration 721 / 882) loss: 1.051474\n",
      "(Iteration 741 / 882) loss: 1.053695\n",
      "(Iteration 761 / 882) loss: 1.107913\n",
      "(Iteration 781 / 882) loss: 0.861129\n",
      "(Epoch 8 / 9) train acc: 0.631000; val_acc: 0.525000\n",
      "(Iteration 801 / 882) loss: 1.048166\n",
      "(Iteration 821 / 882) loss: 1.002725\n",
      "(Iteration 841 / 882) loss: 0.978119\n",
      "(Iteration 861 / 882) loss: 0.952616\n",
      "(Iteration 881 / 882) loss: 0.939108\n",
      "(Epoch 9 / 9) train acc: 0.660000; val_acc: 0.561000\n",
      "\n",
      "Solver without batch norm:\n",
      "(Iteration 1 / 882) loss: 2.302510\n",
      "(Epoch 0 / 9) train acc: 0.092000; val_acc: 0.095000\n",
      "(Iteration 21 / 882) loss: 2.099244\n",
      "(Iteration 41 / 882) loss: 1.836317\n",
      "(Iteration 61 / 882) loss: 1.826721\n",
      "(Iteration 81 / 882) loss: 1.573752\n",
      "(Epoch 1 / 9) train acc: 0.426000; val_acc: 0.399000\n",
      "(Iteration 101 / 882) loss: 1.679803\n",
      "(Iteration 121 / 882) loss: 1.586913\n",
      "(Iteration 141 / 882) loss: 1.564512\n",
      "(Iteration 161 / 882) loss: 1.528342\n",
      "(Iteration 181 / 882) loss: 1.488631\n",
      "(Epoch 2 / 9) train acc: 0.459000; val_acc: 0.470000\n",
      "(Iteration 201 / 882) loss: 1.485641\n",
      "(Iteration 221 / 882) loss: 1.523917\n",
      "(Iteration 241 / 882) loss: 1.461310\n",
      "(Iteration 261 / 882) loss: 1.494706\n",
      "(Iteration 281 / 882) loss: 1.478942\n",
      "(Epoch 3 / 9) train acc: 0.514000; val_acc: 0.495000\n",
      "(Iteration 301 / 882) loss: 1.371357\n",
      "(Iteration 321 / 882) loss: 1.418664\n",
      "(Iteration 341 / 882) loss: 1.325280\n",
      "(Iteration 361 / 882) loss: 1.346401\n",
      "(Iteration 381 / 882) loss: 1.401142\n",
      "(Epoch 4 / 9) train acc: 0.538000; val_acc: 0.506000\n",
      "(Iteration 401 / 882) loss: 1.359853\n",
      "(Iteration 421 / 882) loss: 1.260342\n",
      "(Iteration 441 / 882) loss: 1.320447\n",
      "(Iteration 461 / 882) loss: 1.239593\n",
      "(Iteration 481 / 882) loss: 1.248311\n",
      "(Epoch 5 / 9) train acc: 0.556000; val_acc: 0.541000\n",
      "(Iteration 501 / 882) loss: 1.230428\n",
      "(Iteration 521 / 882) loss: 1.219983\n",
      "(Iteration 541 / 882) loss: 1.245683\n",
      "(Iteration 561 / 882) loss: 1.247704\n",
      "(Iteration 581 / 882) loss: 1.212712\n",
      "(Epoch 6 / 9) train acc: 0.593000; val_acc: 0.514000\n",
      "(Iteration 601 / 882) loss: 1.206587\n",
      "(Iteration 621 / 882) loss: 1.158188\n",
      "(Iteration 641 / 882) loss: 1.155168\n",
      "(Iteration 661 / 882) loss: 1.170429\n",
      "(Iteration 681 / 882) loss: 1.145335\n",
      "(Epoch 7 / 9) train acc: 0.583000; val_acc: 0.528000\n",
      "(Iteration 701 / 882) loss: 1.243557\n",
      "(Iteration 721 / 882) loss: 1.142134\n",
      "(Iteration 741 / 882) loss: 1.050706\n",
      "(Iteration 761 / 882) loss: 1.073243\n",
      "(Iteration 781 / 882) loss: 1.152397\n",
      "(Epoch 8 / 9) train acc: 0.615000; val_acc: 0.534000\n",
      "(Iteration 801 / 882) loss: 1.142842\n",
      "(Iteration 821 / 882) loss: 1.058768\n",
      "(Iteration 841 / 882) loss: 1.107796\n",
      "(Iteration 861 / 882) loss: 1.080666\n",
      "(Iteration 881 / 882) loss: 1.111837\n",
      "(Epoch 9 / 9) train acc: 0.606000; val_acc: 0.498000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "# Try training a very deep net with batchnorm.\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "# small_data = {\n",
    "#   'X_train': data['X_train'][:num_train],\n",
    "#   'y_train': data['y_train'][:num_train],\n",
    "#   'X_val': data['X_val'],\n",
    "#   'y_val': data['y_val'],\n",
    "# }\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:],\n",
    "  'y_train': data['y_train'][:],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "print('Solver with batch norm:')\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=9, batch_size=500,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "print('\\nSolver without batch norm:')\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=9, batch_size=500,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "归一化后，训练的效率会提升很多，训练的效果也更好。\n",
    "\n",
    "为何测试的效果无明显差异？训练周期不够嘛？\n",
    "\n",
    "**原因：数据不够多。加训练大数据量，归一化的效果表现也更好**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization and Initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train eight-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "# Try training a very deep net with batchnorm.\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "    print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "    bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    bn_solver.train()\n",
    "    bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    solver.train()\n",
    "    solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment.\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs. weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs. weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs. weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the weight initialization scale affect models with/without batch normalization differently, and why?\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization and Batch Size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    \n",
    "    # Try training a very deep net with batchnorm.\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "归一化的批次越大，训练效果越好 \n",
    "\n",
    "小批次情况下，由于其均值、方差较样本总体空间的均值、方差有较大概率出现方差、因此归一化会使得输入数据按错误参数归一化，使得效果变差。\n",
    "\n",
    "而大批量数据分布与总体空间更一致，使得归一化的优势凸显，训练正确率更高。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_forward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means: [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:  [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "  means: [-4.81096644e-16  0.00000000e+00  7.40148683e-17 -5.55111512e-16]\n",
      "  stds:  [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "  means: [5. 5. 5. 5.]\n",
      "  stds:  [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization.\n",
    "\n",
    "# Simulate the forward pass for a two-layer network.\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "# Means should be close to zero and stds close to one.\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma.\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.433615657860454e-09\n",
      "dgamma error:  4.519489546032799e-12\n",
      "dbeta error:  2.276445013433725e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass.\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "# You should expect to see relative errors between 1e-12 and 1e-8.\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and Batch Size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "2。当模型尺寸太小，会使得从输出数据评估的模型输出统计参数与真实分布存在较大偏差，使得正则化后的效果变差\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
